Augmented Reality: A new way of learning?

About Denise Pires
Denise Pires is currently a MA New Media student at the University of Amsterdam. She has a bachelor degree in Interaction Design (Hogeschool van Rotterdam, Communication and Multimedia Design.
Denise works as an usability researcher at valspat | usability onderzoek in Amsterdam. She does research on the efficiency and effectiveness of websites, games and other interactive products. Her main fields of interest are the digital identity, Augmented Reality and RFID.

Augmented Reality (AR) is no longer science fiction. The usage of AR is rising in our society. What is AR aiming on? On the enrichment of physical spaces with computer generated images and the availability of location based content. AR can be a strong potential for traditional ways of learning. But what does AR do with the withdrawal of knowledge and the processing of this knowledge? What should we take into account if we want to use AR effectively for educational purposes?

Situated Cognition

One of the theories that have been developed in cognitive science is the ‘situativity theory of cognition’. The origin of this theory can be found in psychology. Situated cognition looks at the human cognition and proposes that the user actively absorbs knowledge when he or she makes a connection between facts learned and the environment in which events related to these facts take place (Greeno 1998: p.2). The environment plays an important role in the active withdrawal of knowledge and information:

It is not enough to present users a list of dry facts. Knowledge must be linked to practices that create a deep impact on users. You must create opportunities for users to put in to practice the dry facts that they have assembled (Kurt Squire et al, 2007). The learning process is shaped further when implemented in an activity.

A nice example of an AR system is Wikitude. It extracts content from Wikipedia and presents the user with data about their surroundings, nearby landmarks, and other points of interest by overlaying information on the real-time camera view of a smart-phone. Layar is also a nice example. This AR application shows what is around you by displaying real time digital information on top of reality through the camera of your mobile phone.

Non-lineair learning

The withdrawal of knowledge in new media environments generally take place on a non-linear way. It is unlike reading an article or a book. You do not follow a path from A to Z, but you choose your own pathways. As mentioned in a previous article we are like ‘trippers jumping from one link to another’ examining content that can disseminate from content read before. This also applies to AR systems. Content can differ from each other.

This causes a ‘cognitive switch’; a new way of recording and processing knowledge’ (Calleja, 2004). While processing information, information is always changing. Processes of assimilation and withdrawal are constantly in play. Our brains try to create order by giving us the illusion that the information that we assimilate is in some way linear by making links between information that has been stored (Calleja, 2004: p.5). Basically we are learning by making connections between pieces of information that seem to have no relation with each other on forehand.

AR: a strong potential?

In AR systems we learn on context-dependent ways and through trial and error. It provides a safe way to explore: if you fail it will not have ‘fatal’ consequences. Take for instance AR systems in which students can learn how to perform a heart surgery. Abstract knowledge can be applied / practiced and effects are immediately spotted. Besides, fatal errors are out of the question. This can have a positive effect on the learning process. But what issues should you take into account?

Pay close attention to how you involve the environment while offering contextual information. Link information directly to practices in the environment.

Provide users more contextual information instead of only abstract information. AR systems can only operate efficiently when information can be linked directly to objects in the physical space.

Offer information at the right time and at the right spot so that no accumulation of information within the system occurs and filter available information about objects in physical spaces by using algorithms. This would otherwise have a disadvantageous effect on the efficiency of the system.

So AR can become a strong potential for the learning process. However, rather than being a technology that is suitable for the transmission of abstract information, AR is a platform on which you can learn on a situated way.